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Artykuły w czasopismach na temat „Ordered Probit Model”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 21 lutego 2023

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1

Johnston, Carla, James McDonald i Kramer Quist. "A generalized ordered Probit model". Communications in Statistics - Theory and Methods 49, nr 7 (23.01.2019): 1712–29. http://dx.doi.org/10.1080/03610926.2019.1565780.

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Daykin, Anne R., i Peter G. Moffatt. "Analyzing Ordered Responses: A Review of the Ordered Probit Model". Understanding Statistics 1, nr 3 (2.08.2002): 157–66. http://dx.doi.org/10.1207/s15328031us0103_02.

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Wang, Xiaokun (Cara), i Kara M. Kockelman. "Application of Dynamic Spatial Ordered Probit Model". Transportation Research Record: Journal of the Transportation Research Board 2136, nr 1 (styczeń 2009): 45–56. http://dx.doi.org/10.3141/2136-06.

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Rifaat, S. M., i H. C. Chin. "Accident severity analysis using ordered probit model". Journal of Advanced Transportation 41, nr 1 (wrzesień 2007): 91–114. http://dx.doi.org/10.1002/atr.5670410107.

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Park, Seongmin, i Juneyoung Park. "Multilevel Mixed-Effects Models to Identify Contributing Factors on Freight Vehicle Crash Severity". Sustainability 14, nr 19 (20.09.2022): 11804. http://dx.doi.org/10.3390/su141911804.

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Freight vehicle crashes are more serious than regular vehicle crashes because they are likely to lead to major damage and injury once they occur; therefore, countermeasures are needed. The fatality rate from freight vehicle crashes is 1.5 times higher than that of all other accidents, and the death rate from expressway freight vehicle crashes continues to increase. In this study, the ten-freight-vehicle crash severity models (the ordered logit and probit model, the multinomial logit and probit model, mixed-effects logit and probit model, random-effects ordered logit and probit model, and multilevel mixed-effects ordered logit and probit model) are used to analyze the freight vehicle crash severity factors. The model was constructed using data collected from expressways over eight years, and 13 factors were derived to increase the severity of crashes and 7 factors to reduce the severity of crashes. As a result of comparing the 10 constructed models using AIC and BIC, the multilevel mixed-effects ordered probit model showed the best performance. It is expected that it can contribute to improving the safety of freight vehicles in the expressway section by utilizing factors related to the severity of crashes derived from this study.

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6

Cameletti, Michela, Valerio De Rubeis, Clarissa Ferrari, Paola Sbarra i Patrizia Tosi. "An ordered probit model for seismic intensity data". Stochastic Environmental Research and Risk Assessment 31, nr 7 (12.05.2016): 1593–602. http://dx.doi.org/10.1007/s00477-016-1260-4.

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7

Kim, Haksoon. "Stock price discreteness and clustering: decimals and ordered probit model". Journal of Financial Regulation and Compliance 22, nr 1 (4.02.2014): 49–60. http://dx.doi.org/10.1108/jfrc-07-2012-0025.

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Purpose – The purpose of this paper is to revisit the ordered probit model of Hausman et al. after the NYSE decimalization. Design/methodology/approach – The changed ordered probit model. Findings – The model can somewhat capture the different impact of trading-related “explanatory” variables on price changes among three different decimals but does not explain much about price discreteness and irregular transaction intervals among the existing models of stock price discreteness. Overall 1/16th and 1/24th range of the dependent variable is better explained by trading-related explanatory variables than 1/8th range of the dependent variable for small firms and there is not much difference in large firms among three decimals. The results imply that finer specification in decimalization and smaller firm size matters in trading after the decimalization project. Originality/value – First paper to revisit the ordered probit model of Hausman et al. after the NYSE decimalization.

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8

Zeng, Fan, Xiao Cao i Huan-Ming Zhang. "Analysis of Digital Life Effect of Residents’ Trust Based on Multivariate Discrete Choice Model". Journal of Function Spaces 2022 (18.07.2022): 1–14. http://dx.doi.org/10.1155/2022/1801399.

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In view of the impact of digital life on residents’ trust, firstly, based on the data of China Household Finance Survey (CHFS), this paper analyzes the change of residents’ trust in the era of data flooding. Through basic research and marginal effect analysis, it is found that digital life enhances residents’ trust. Secondly, the robustness of the conclusion is confirmed by constructing a series of models such as ordered probit of instrumental variable (IV-O probit), ordered probit of propensity score matching method (PSM-O probit), and ordered probit Heckman (O-probit-Heckman). Finally, through the mechanism test, it is concluded that digital life can enhance residents’ trust by increasing social activities and forming an inclusive social environment. It will also increase trust by obtaining information and increasing their cognitive level. The conclusion that digital life can enhance residents’ trust is more prominent among urban residents, nonpoor Hukou residents, and Han residents. Further research shows that the breadth and depth of digital life have a positive impact on residents’ trust, and digital life can improve residents’ risk tolerance.

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9

Park, Jong Hee. "Changepoint Analysis of Binary and Ordinal Probit Models: An Application to Bank Rate Policy Under the Interwar Gold Standard". Political Analysis 19, nr 2 (2011): 188–204. http://dx.doi.org/10.1093/pan/mpr007.

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In this paper, I introduce changepoint models for binary and ordered time series data based on Chib's hidden Markov model. The extension of the changepoint model to a binary probit model is straightforward in a Bayesian setting. However, detecting parameter breaks from ordered regression models is difficult because ordered time series data often have clustering along the break points. To address this issue, I propose an estimation method that uses the linear regression likelihood function for the sampling of hidden states of the ordinal probit changepoint model. The marginal likelihood method is used to detect the number of hidden regimes. I evaluate the performance of the introduced methods using simulated data and apply the ordinal probit changepoint model to the study of Eichengreen, Watson, and Grossman on violations of the “rules of the game” of the gold standard by the Bank of England during the interwar period.

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10

Lin, Zijing, i Wei “David” Fan. "Bicycle Ridership Using Crowdsourced Data: Ordered Probit Model Approach". Journal of Transportation Engineering, Part A: Systems 146, nr 8 (sierpień 2020): 04020076. http://dx.doi.org/10.1061/jtepbs.0000399.

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Brooks, Robert, i Shelley Claire Naylor. "An ordered probit model of Morningstar individual stock ratings". Applied Financial Economics Letters 4, nr 5 (26.09.2008): 341–45. http://dx.doi.org/10.1080/17446540701736002.

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Alsakka, Rasha, i Owain ap Gwilym. "A random effects ordered probit model for rating migrations". Finance Research Letters 7, nr 3 (wrzesień 2010): 140–47. http://dx.doi.org/10.1016/j.frl.2010.02.004.

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13

Dale, David, i Andrei Sirchenko. "Estimation of nested and zero-inflated ordered probit models". Stata Journal: Promoting communications on statistics and Stata 21, nr 1 (marzec 2021): 3–38. http://dx.doi.org/10.1177/1536867x211000002.

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We introduce three new commands—nop, ziop2, and ziop3—for the estimation of a three-part nested ordered probit model, the two-part zero-inflated ordered probit models of Harris and Zhao (2007, Journal of Econometrics 141: 1073–1099) and Brooks, Harris, and Spencer (2012, Economics Letters 117: 683–686), and a three-part zero-inflated ordered probit model of Sirchenko (2020, Studies in Nonlinear Dynamics and Econometrics 24: 1) for ordinal outcomes, with both exogenous and endogenous switching. The three-part models allow the probabilities of positive, neutral (zero), and negative outcomes to be generated by distinct processes. The zero-inflated models address a preponderance of zeros and allow them to emerge in different latent regimes. We provide postestimation commands to compute probabilistic predictions and various measures of their accuracy, to assess the goodness of fit, and to perform model comparison using the Vuong test (Vuong, 1989, Econometrica 57: 307–333) with the corrections based on the Akaike and Schwarz information criteria. We investigate the finite-sample performance of the maximum likelihood estimators by Monte Carlo simulations, discuss the relations among the models, and illustrate the new commands with an empirical application to the U.S. federal funds rate target.

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14

Wang, Xiaokun, i Kara M. Kockelman. "BAYSIAN INFERENCE FOR ORDERED RESPONSE DATA WITH A DYNAMIC SPATIAL-ORDERED PROBIT MODEL". Journal of Regional Science 49, nr 5 (grudzień 2009): 877–913. http://dx.doi.org/10.1111/j.1467-9787.2009.00622.x.

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15

Bagozzi, Benjamin E., i Bumba Mukherjee. "A Mixture Model for Middle Category Inflation in Ordered Survey Responses". Political Analysis 20, nr 3 (2012): 369–86. http://dx.doi.org/10.1093/pan/mps020.

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Recent research finds that, for social desirability reasons, uninformed individuals disproportionately give “neither agree nor disagree” type responses to survey attitude questions, even when a “do not know” option is available. Such “face-saving” responses inflate the indifference (i.e., middle) categories of ordered attitude variables with nonordered responses. When such inflation occurs within the middle category of one's ordered dependent variable, estimates from ordered probit (and ordered logit) models are likely to be unreliable and inefficient. This article develops a set of mixture models that estimate and account for the presence of “face-saving” responses in middle categories of ordered survey response variables, and applies these models to (1) simulated data and (2) a commonly studied survey question measuring attitudes toward European Union (EU) membership among individuals in EU-candidate countries. Results from the survey data set and the Monte Carlo experiments suggest that, when middle category inflation is present in one's ordered dependent variable, the estimates obtained from middle category mixture models are less biased than—and in some cases substantively distinct from—the estimates obtained from “naive” ordered probit models.

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16

Harrison, R. Wes, Jeffrey Gillespie i Deacue Fields. "Analysis of Cardinal and Ordinal Assumptions in Conjoint Analysis". Agricultural and Resource Economics Review 34, nr 2 (październik 2005): 238–52. http://dx.doi.org/10.1017/s106828050000839x.

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Of twenty-three agricultural economics conjoint analyses conducted between 1990 and 2001, seventeen used interval-rating scales, with estimation procedures varying widely. This study tests cardinality assumptions in conjoint analysis when interval-rating scales are used, and tests whether the ordered probit or two-limit tobit model is the most valid. Results indicate that cardinality assumptions are invalid, but estimates of the underlying utility scale for the two models do not differ. Thus, while the ordered probit model is theoretically more appealing, the two-limit tobit model may be more useful in practice, especially in cases with limited degrees of freedom, such as with individual-level conjoint models.

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17

Gurmu, Shiferaw, i Getachew A. Dagne. "Bayesian Approach to Zero-Inflated Bivariate Ordered Probit Regression Model, with an Application to Tobacco Use". Journal of Probability and Statistics 2012 (2012): 1–26. http://dx.doi.org/10.1155/2012/617678.

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This paper presents a Bayesian analysis of bivariate ordered probit regression model with excess of zeros. Specifically, in the context of joint modeling of two ordered outcomes, we develop zero-inflated bivariate ordered probit model and carry out estimation using Markov Chain Monte Carlo techniques. Using household tobacco survey data with substantial proportion of zeros, we analyze the socioeconomic determinants of individual problem of smoking and chewing tobacco. In our illustration, we find strong evidence that accounting for excess zeros provides good fit to the data. The example shows that the use of a model that ignores zero-inflation masks differential effects of covariates on nonusers and users.

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18

Glewwe, P. "A test of the normality assumption in ordered probit model". Econometric Reviews 16, nr 1 (styczeń 1997): 1–19. http://dx.doi.org/10.1080/07474939708800369.

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19

Storchmann, Karl. "English weather and Rhine wine quality: An ordered probit model". Journal of Wine Research 16, nr 2 (sierpień 2005): 105–20. http://dx.doi.org/10.1080/09571260500327648.

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20

Groot, Wim, i Henriëtte Maassen van den Brink. "Firm-related training tracks: a random effects ordered probit model". Economics of Education Review 22, nr 6 (grudzień 2003): 581–89. http://dx.doi.org/10.1016/s0272-7757(03)00030-x.

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Garrido, Rui, Ana Bastos, Ana de Almeida i José Paulo Elvas. "Prediction of Road Accident Severity Using the Ordered Probit Model". Transportation Research Procedia 3 (2014): 214–23. http://dx.doi.org/10.1016/j.trpro.2014.10.107.

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22

Shi, Lei. "Bayesian analysis of multivariate ordered probit model with individual heterogeneity". AStA Advances in Statistical Analysis 104, nr 4 (23.06.2020): 649–65. http://dx.doi.org/10.1007/s10182-020-00369-2.

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Kim, Kyungmann. "A bivariate cumulative probit regression model for ordered categorical data". Statistics in Medicine 14, nr 12 (30.06.1995): 1341–52. http://dx.doi.org/10.1002/sim.4780141207.

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Munkin, Murat K., i Pravin K. Trivedi. "Bayesian analysis of the ordered probit model with endogenous selection". Journal of Econometrics 143, nr 2 (kwiecień 2008): 334–48. http://dx.doi.org/10.1016/j.jeconom.2007.11.001.

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Yue, Chengyan, Charles R. Hall, Bridget K. Behe, Benjamin L. Campbell, Jennifer H. Dennis i Roberto G. Lopez. "Are Consumers Willing to Pay More for Biodegradable Containers Than for Plastic Ones? Evidence from Hypothetical Conjoint Analysis and Nonhypothetical Experimental Auctions". Journal of Agricultural and Applied Economics 42, nr 4 (listopad 2010): 757–72. http://dx.doi.org/10.1017/s1074070800003941.

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This study used and compared hypothetical conjoint analysis and nonhypothetical experimental auctions to elicit floral customers' willingness to pay for biodegradable plant containers. The results of the study show that participants were willing to pay a price premium for biodegradable containers, but the premium is not the same for different types of containers. This article also shows the mixed ordered probit model generates more accurate results when analyzing the conjoint analysis Internet survey data than the ordered probit model.

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Darban Khales, Sina, Mehmet Metin Kunt i Branislav Dimitrijevic. "Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit". Canadian Journal of Civil Engineering 47, nr 11 (listopad 2020): 1249–57. http://dx.doi.org/10.1139/cjce-2019-0394.

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The study analyzed injury severity of teenage and older drivers using 2015–2016 crash data from New Mexico. The fitness of the random-parameter ordered probit models developed for each age group was tested using likelihood ratio, comparing them to a unified model that combines both age groups, as well as comparing the random-parameter to fixed-parameter ordered probit for each age group. In both cases separate random-parameter ordered probit provided better results. It was found that vehicle type and age, lighting condition, alcohol or drug use, speeding, and seatbelt use were significant both for the teenage and older driver injury severity. The weather condition and gender were significant only in the teenage driver model, while driver inattention was significant for older drivers. The impacts of crash factors on injury severity was analyzed using marginal effects. The results indicate notable differences in the effects of contributing factors on driver injury severity between teenage and older drivers, including the sensitivity to changes in the mutual predictor parameter values.

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Reardon, Sean F., Benjamin R. Shear, Katherine E. Castellano i Andrew D. Ho. "Using Heteroskedastic Ordered Probit Models to Recover Moments of Continuous Test Score Distributions From Coarsened Data". Journal of Educational and Behavioral Statistics 42, nr 1 (22.10.2016): 3–45. http://dx.doi.org/10.3102/1076998616666279.

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Test score distributions of schools or demographic groups are often summarized by frequencies of students scoring in a small number of ordered proficiency categories. We show that heteroskedastic ordered probit (HETOP) models can be used to estimate means and standard deviations of multiple groups’ test score distributions from such data. Because the scale of HETOP estimates is indeterminate up to a linear transformation, we develop formulas for converting the HETOP parameter estimates and their standard errors to a scale in which the population distribution of scores is standardized. We demonstrate and evaluate this novel application of the HETOP model with a simulation study and using real test score data from two sources. We find that the HETOP model produces unbiased estimates of group means and standard deviations, except when group sample sizes are small. In such cases, we demonstrate that a “partially heteroskedastic” ordered probit (PHOP) model can produce estimates with a smaller root mean squared error than the fully heteroskedastic model.

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Ntemi, Myrsini, i Constantine Kotropoulos. "Online hotel rating prediction through a dynamic weighted ordered probit model". Digital Signal Processing 120 (styczeń 2022): 103310. http://dx.doi.org/10.1016/j.dsp.2021.103310.

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Sy, Hamath A., Merle D. Faminow, Gary V. Johnson i Gary Crow. "Estimating the Values of Cattle Characteristics Using an Ordered Probit Model". American Journal of Agricultural Economics 79, nr 2 (maj 1997): 463–76. http://dx.doi.org/10.2307/1244144.

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Lim, Joonbeom, Soobeom Lee, Dukgeun Yun i Jaehong Park. "Crash Severity Impact of Fixed Roadside Objects using Ordered Probit Model". International Journal of Highway Engineering 18, nr 6 (15.12.2016): 173–80. http://dx.doi.org/10.7855/ijhe.2016.18.6.173.

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Yuliar Rezika, Wida, Sapto Priyanto i Muhammad Zudhy Irawan. "Estimation urban railway demand in Yogyakarta using Bivariate Ordered Probit Model". MATEC Web of Conferences 181 (2018): 02009. http://dx.doi.org/10.1051/matecconf/201818102009.

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The urban railway system is believed to solve transportation problems caused by the high growth of private vehicles and urbanization. This study is going to analyze the potential demand for the urban railway in Yogyakarta, Indonesia based on bivariate ordered probit model. The survey of preference stated with 120 samples conducted in Yogyakarta. The model of train demand is distinguished between public transport users and private vehicle users using seven scenarios. In-train travel time, waiting time, tariff, and ticketing discount for students are four factors considered in the model. The demand model shows that in-train travel time is the most important factor influence for train demand. Meanwhile, the scenario result reveals that respondents except student are willing to pay more to obtain shorter travel time, students who use private vehicle are reluctant to shift into the train, and ticketing discount brings no effect to stimulate them to use the train.

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Chen, Jo-Hui, i Wan-Chieh Tsai. "A comparison of international income inequality: an ordered probit model analysis". Applied Economics 44, nr 13 (maj 2012): 1701–16. http://dx.doi.org/10.1080/00036846.2011.554371.

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Stegmueller, Daniel. "Modeling Dynamic Preferences: A Bayesian Robust Dynamic Latent Ordered Probit Model". Political Analysis 21, nr 3 (2013): 314–33. http://dx.doi.org/10.1093/pan/mpt001.

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Much politico-economic research on individuals' preferences is cross-sectional and does not model dynamic aspects of preference or attitude formation. I present a Bayesian dynamic panel model, which facilitates the analysis of repeated preferences using individual-level panel data. My model deals with three problems. First, I explicitly include feedback from previous preferences taking into account that available survey measures of preferences are categorical. Second, I model individuals' initial conditions when entering the panel as resulting from observed and unobserved individual attributes. Third, I capture unobserved individual preference heterogeneity both via standard parametric random effects and a robust alternative based on Bayesian nonparametric density estimation. I use this model to analyze the impact of income and wealth on preferences for government intervention using the British Household Panel Study from 1991 to 2007.

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Zong, Fang, Huiyong Zhang, Hongguo Xu, Xiumei Zhu i Lu Wang. "Predicting Severity and Duration of Road Traffic Accident". Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/547904.

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This paper presents a model system to predict severity and duration of traffic accidents by employing Ordered Probit model and Hazard model, respectively. The models are estimated using traffic accident data collected in Jilin province, China, in 2010. With the developed models, three severity indicators, namely, number of fatalities, number of injuries, and property damage, as well as accident duration, are predicted, and the important influences of related variables are identified. The results indicate that the goodness-of-fit of Ordered Probit model is higher than that of SVC model in severity modeling. In addition, accident severity is proven to be an important determinant of duration; that is, more fatalities and injuries in the accident lead to longer duration. Study results can be applied to predictions of accident severity and duration, which are two essential steps in accident management process. By recognizing those key influences, this study also provides suggestive results for government to take effective measures to reduce accident impacts and improve traffic safety.

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Chiburis, Richard, i Michael Lokshin. "Maximum Likelihood and Two-Step Estimation of an Ordered-Probit Selection Model". Stata Journal: Promoting communications on statistics and Stata 7, nr 2 (czerwiec 2007): 167–82. http://dx.doi.org/10.1177/1536867x0700700202.

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We discuss the estimation of a regression model with an ordered-probit selection rule. We have written a Stata command, oheckman, that computes two-step and full-information maximum-likelihood estimates of this model. Using Monte Carlo simulations, we compare the performances of these estimators under various conditions.

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Ghanbarnejad, Amin, Atefeh Homayuni, Zahra Hosseini i Abdolhossain Madani. "Smoking Behavior Among Students: Using Health Belief Model and Zero-Inflated Ordered Probit Model". Tobacco and Health 1, nr 2 (30.03.2022): 74–82. http://dx.doi.org/10.34172/thj.2022.12.

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Background: Smoking is increasing among adolescents and young adults. Adolescents’ smoking can predict frequent smoking in early adulthood. This study aimed to explore the predictors of smoking among high school students using health belief model (HBM). Materials and Methods: In this cross-sectional study conducted in 2019 in Bandar-Abbas city, South of Iran, 444 male high-school students aged 15-19 years (mean age: 16.7±0.85) were explored. Data were collected using a researcher-made questionnaire consisted of two main sections: sociodemographic characteristics and HBM constructs. The zero-inflated ordered probit (ZIOP) model was adopted for investigating the association between HBM constructs and smoking behavior. Results: Results indicated that 82% of the participants never smoked cigarette. The results of ZIOP model showed that the knowledge (P=0.026), susceptibility (P<0.001), severity (P=0.035), benefits (P=0.004), and cues to action (P=0.019) had significant effects on smoking cigarette after being adjusted for other covariates (i.e., age, parents’ education, losing one of the parents). Moreover, having a smoker friend was found to be an inflation factor (P<0.001). Adolescents with smoking friends were 44% less likely to avoid smoking. Conclusion: It was concluded that having a smoker friend, knowledge, susceptibility, severity, benefits, and cues to action had considerable predictive capacity for predicting smoking attitude. Therefore, it was recommended that these factors should be seriously considered when designing educational programs with the aim of reducing adolescent smoking.

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Chen, Longmei, Alan T. K. Wan, Geoffrey Tso i Xinyu Zhang. "A model averaging approach for the ordered probit and nested logit models with applications". Journal of Applied Statistics 45, nr 16 (21.03.2018): 3012–52. http://dx.doi.org/10.1080/02664763.2018.1450367.

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Nair, Gopindra S., Chandra R. Bhat, Ram M. Pendyala, Becky P. Y. Loo i William H. K. Lam. "On the Use of Probit-Based Models for Ranking Data Analysis". Transportation Research Record: Journal of the Transportation Research Board 2673, nr 4 (kwiecień 2019): 229–40. http://dx.doi.org/10.1177/0361198119838987.

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In consumer surveys, more information per response regarding preferences of alternatives may be obtained if individuals are asked to rank alternatives instead of being asked to select only the most-preferred alternative. However, the latter method continues to be the common method of preference elicitation. This is because of the belief that ranking of alternatives is cognitively burdensome. In addition, the limited research on modeling ranking data has been based on the rank ordered logit (ROL) model. In this paper, we show that a rank ordered probit (ROP) model can better utilize ranking data information, and that the prevalent view of ranking data as not being reliable (because of the attenuation of model coefficients with rank depth) may be traced to the use of a misspecified ROL model rather than to any cognitive burden considerations.

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Weng, Jinxian, Guorong Li, Tian Chai i Dong Yang. "Evaluation of Two-Ship Collision Severity using Ordered Probit Approaches". Journal of Navigation 71, nr 4 (1.02.2018): 822–36. http://dx.doi.org/10.1017/s0373463317000996.

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This study develops an ordered probit model to evaluate the factors influencing two-ship collision severity using ten years’ ship collision accident data from Fujian sea areas. The model results show that the involvement of big ships has the largest impact in increasing the probability of a serious or very serious accident, followed by the involvement of fishing vessels. There will be a bigger probability of a serious accident if both ships involved in the collision are cargo ships. We found that the season of spring, poor visibility and night time periods are more likely to be factors in high severity levels of ship collision. The results also reveal that lookout failure plays a decisive role in increasing serious accident risk compared with other types of human errors. The results of this study may be beneficial for policy-makers in proposing efficient strategies to reduce the likelihood of serious ship collisions.

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Weiss, Andrew A. "A Bivariate Ordered Probit Model with Truncation: Helmet Use and Motorcycle Injuries". Applied Statistics 42, nr 3 (1993): 487. http://dx.doi.org/10.2307/2986327.

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Choi, Jung Woo, i Ki Jung Kum. "Analysis of Factors influencing Severity of Motorcycle Accidents using Ordered Probit Model". Journal of the Korean Society of Road Engineers 16, nr 5 (16.10.2014): 143–54. http://dx.doi.org/10.7855/ijhe.2014.16.5.143.

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Kim, Taeho, i Minho Park. "Factors Analysis on The Accident Severity of Children using Ordered Probit Model". International Journal of Highway Engineering 21, nr 3 (30.06.2019): 97–107. http://dx.doi.org/10.7855/ijhe.2019.21.3.097.

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Xu, Xuecai, Dong Huang i Fengjun Guo. "Addressing spatial heterogeneity of injury severity using Bayesian multilevel ordered probit model". Research in Transportation Economics 80 (maj 2020): 100748. http://dx.doi.org/10.1016/j.retrec.2019.100748.

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Md, Tanvir Siddique. "Accident severity analysis on national highways in Bangladesh using ordered probit model". Scientific Research and Essays 13, nr 14 (15.09.2018): 148–57. http://dx.doi.org/10.5897/sre2018.6584.

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Bhowmik, Tanmoy, Shamsunnahar Yasmin i Naveen Eluru. "A multilevel generalized ordered probit fractional split model for analyzing vehicle speed". Analytic Methods in Accident Research 21 (marzec 2019): 13–31. http://dx.doi.org/10.1016/j.amar.2018.12.001.

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